Dialysis machines are known for use in the treatment of renal disease. Two types of dialysis methods are hemodialysis (HD) and peritoneal dialysis (PD). During hemodialysis, the patient's blood is passed through a dialyzer of a hemodialysis machine while also passing dialysate through the dialyzer. A semi-permeable membrane in the dialyzer separates the blood from the dialysate within the dialyzer and allows diffusion and osmosis exchanges to take place between the dialysate and the blood stream. During peritoneal dialysis, the patient's peritoneal cavity is periodically infused with dialysate or dialysis solution. The membranous lining of the patient's peritoneum acts as a natural semi-permeable membrane that allows diffusion and osmosis exchanges to take place between the solution and the blood stream. Automated peritoneal dialysis machines, also called PD cyclers, are designed to control the entire peritoneal dialysis process so that it can be performed at home, usually overnight, without clinical staff in attendance.
Many different types of systems benefit from adaptive operation that is provided when used with artificial intelligence. In such systems, operational parameters are modified based on data inputs thereto that provide feedback to the artificial intelligence systems. For example, a speech recognition system may adapt and improve based on data input thereto that indicates successes and failures. The adaptive optimizations may be provided by artificial intelligence processing that revises operation states of the speech recognition system. Although dialysis systems could benefit from adaptive optimizations provided by artificial intelligence, such adaptions, on their own, run the risk of causing a dialysis system to not operate as originally intended, to the possible detriment of patients. In addition, since design and operation of dialysis systems are regulated by the Food and Drug Administration (FDA) in the United States (other countries have similar regulatory systems), adaptive optimizations provided by artificial intelligence may cause the system to deviate from approved design parameters in a way that is not traceable/repeatable so that there is no integrity for the state-related data of a module following adaptation.
Accordingly, it is desirable to provide a mechanism to facilitate adaptation of modules of a dialysis system using artificial intelligence in a way that maintains the dialysis system in compliance with acceptable operational parameters and regulatory requirements and provides integrity for state-related data.